To develop a deep transfer learning method that incorporates four-dimensional (4D) information in dynamic contrast-enhanced (DCE) MRI to classify benign and malignant breast lesions.
The retrospective dataset is composed of 1990 distinct lesions (1494 malignant and 496 benign) from 1979 women (mean age, 47 years ± 10). Lesions were split into a training and validation set of 1455 lesions (acquired in 2015-2016) and an independent test set of 535 lesions (acquired in 2017). Features were extracted from a convolutional neural network (CNN), and lesions were classified as benign or malignant using support vector machines. https://www.selleckchem.com/products/smoothened-agonist-sag-hcl.html Volumetric information was collapsed into two dimensions by taking the maximum intensity projection (MIP) at the image level or feature level within the CNN architecture. Performances were evaluated using the area under the receiver operating characteristic curve (AUC) as the figure of merit and were compared using the DeLong test.
The image MIP and feature MIP methods yielded AUCs of 0.9Enhanced, Supervised learning, Support vector machines (SVM), Transfer learning, Volume Analysis ©?RSNA, 2021.To organize a multi-institute knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
A dataset partition consisting of three-dimensional knee MRI from 88 retrospective patients at two time points (baseline and 1-year follow-up) with ground truth articular (femoral, tibial, and patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated against ground truth segmentations using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a holdout test set. Similarities in automated segmentations were measured using pairwise Dice coefficient correlations. Articular cartilage thickness was computed longitudinally and with scans. Correlation between thickness error and segmentation metrics was measured using the Pearson correlation coefficient. Two empirical upper bounds for ensemee also the commentary by Elhalawani and Mak in this issue.Cartilage, Knee, MR-Imaging, Segmentation © RSNA, 2020
Diverse networks learned to segment the knee similarly, where high segmentation accuracy did not correlate with cartilage thickness accuracy and voting ensembles did not exceed individual network performance.See also the commentary by Elhalawani and Mak in this issue.Keywords Cartilage, Knee, MR-Imaging, Segmentation © RSNA, 2020Supplemental material is available for this article.In December 2019, a working group of the European Academy of Microbiology assembled to discuss various aspects of vaccines and vaccinations. The meeting was organised by Jörg Hacker and Eliora Z. Ron and took place in the offices of the Leopoldina (German National Academy of Sciences Leopoldina). Several important issues were addressed and a major part of the discussion focused on the need to develop new vaccines, especially to protect against pathogens that constitute a pandemic threat. Following the rapid and unpredicted spread of COVID-19 in the first seven months of 2020, the need to develop vaccines for pandemic viruses rapidly has been clearly established. Thus, this paper will concentrate on points that were highlighted by the recent COVID-19 pandemic and lessons learnt therefrom.Guidelines on antimicrobial therapy are subject to periodic revision to anticipate changes in the epidemiology of antimicrobial resistance and new scientific knowledge. Changing a policy to a broader spectrum has important consequences on both the individual patient level (e.g. effectiveness, toxicity) and population level (e.g. emerging resistance, costs). By combining both clinical data evaluation and an ethical analysis, we aim to propose a comprehensive framework to guide antibiotic policy dilemmas.
A preliminary framework for decision-making on antimicrobial policy was constructed based on existing literature and panel discussions. Antibiotic policy themes were translated into specific elements that were fitted into this framework. The adapted framework was evaluated in two moral deliberation groups. The moral deliberation sessions were analysed using ATLAS.ti statistical software to categorize arguments and evaluate completeness of the final framework.
The final framework outlines the process of data evaluation, ethical deliberation and decision-making. The first phase is a factual data exploration. In the second phase, perspectives are weighed and the policy of moral preference is formulated. Judgments are made on three levels the individual patient, the patient population and society. In the final phase, feasibility, implementation and re-evaluation are addressed.
The proposed framework facilitates decision-making on antibiotic policy by structuring existing data, identifying knowledge gaps, explicating ethical considerations and balancing interests of the individual and current and future generations.
The proposed framework facilitates decision-making on antibiotic policy by structuring existing data, identifying knowledge gaps, explicating ethical considerations and balancing interests of the individual and current and future generations.Idiopathic intracranial hypertension is a brain disease incorporating cerebrospinal fluid disturbance, increased intracranial pressure and visual failure, but with unknown cause. This study examined a hypothesis that glymphatic function is impaired in idiopathic intracranial hypertension patients. The MRI contrast agent gadobutrol was utilized as a cerebrospinal fluid tracer following intrathecal administration. Consecutive standardized T1 MRI acquisitions over 48?h were done to assess tracer distribution within brain of 15 idiopathic intracranial hypertension patients and 15 reference individuals who were comparable in age and gender distribution. Using FreeSurfer software, we semi-quantified tracer level in multiple brain regions as T1 MRI signal change. The tracer enriched the entire brain of idiopathic intracranial hypertension and reference subjects. In idiopathic intracranial hypertension, tracer enrichment was increased and clearance of tracer delayed from a wide range of brain regions, including both grey and white matter.